code: def init_state(self, x):
      # to support mutiple encoder asr mode, in single encoder mode,
      # convert torch.Tensor to List of torch.Tensor
      if self.num_encs == 1:
          x = [x]
  
      c_list = [self.zero_state(x[0].unsqueeze(0))]
      z_list = [self.zero_state(x[0].unsqueeze(0))]
      for _ in range(1, self.dlayers):
          c_list.append(self.zero_state(x[0].unsqueeze(0)))
          z_list.append(self.zero_state(x[0].unsqueeze(0)))
      # TODO(karita): support strm_index for `asr_mix`
      strm_index = 0
      att_idx = min(strm_index, len(self.att_list) - 1)
      if self.num_encs == 1:
          a = None
          self.att_list[att_idx].reset()  # reset pre-computation of h
      else:
          a = [None] * (self.num_encs + 1)  # atts + han
          for idx in range(self.num_encs + 1):
              # reset pre-computation of h in atts and han
              self.att_list[idx].reset()
      return dict(
          c_prev=c_list[:],
          z_prev=z_list[:],
          a_prev=a,
          workspace=(att_idx, z_list, c_list),
      )
  
file_path: espnet/espnet2/asr/decoder/rnn_decoder.py
imports: import random
  import numpy
  import torch
  import torch.nn.functional
  from typeguard import check_argument_types
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.utils.get_default_kwargs import get_default_kwargs
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, to_device
  from espnet.nets.pytorch_backend.rnn.attentions import initial_att
label_desc: Use image node_id 5 for calling the RNNDecoder obj's underlying member method code with example usage: obj.init_state(x) and returns: dict